Decision-based system identification and adaptive resource allocation

System identification extracts information from a system´s operational data to derive a representative model for the system. Studies of system identification have been concentrated on estimation algorithms and their convergence. Focusing on optimal resource allocation under a given reliability requirement, this paper studies identification complexity and its relations to decision making. Dynamic resource assignments are investigated. Resource allocation algorithms are developed and their convergence properties are established. Illustrative examples demonstrate better resource management than worst-case strategies when our algorithms are applied.